Instructions and logic to vectorize conditional loops

ABSTRACT

A processing device to provide vectorization of conditional loops includes vector physical registers to store a source vector having a first plurality of n data fields, and a destination vector comprising a second plurality of data fields corresponding to the first plurality of data fields, wherein each of the second plurality of data fields corresponds to a mask value in a vector conditions mask. The processing device includes a decode stage to decode a first processor instruction specifying a vector expand operation and a data partition size, and execution units to set elements of the source vector to n count values, obtain a decisions vector, generate the vector conditions mask according to the decisions vector, and copy data from consecutive vector elements in the source vector, into unmasked vector elements of the destination vector, without copying data from the source vector into masked vector elements of the destination vector.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of application Ser. No. 13/731,809,titled “Instructions and Logic to Vectorize Conditional Loops,” filedDec. 31, 2012.

FIELD OF THE DISCLOSURE

The present disclosure pertains to the field of processing logic,microprocessors, and associated instruction set architecture that, whenexecuted by the processor or other processing logic, perform logical,mathematical, or other functional operations. In particular, thedisclosure relates to instructions and logic to provide SIMDvectorization functionality for conditional loops.

BACKGROUND OF THE DISCLOSURE

Modern processors often include instructions to provide operations thatare computationally intensive, but offer a high level of dataparallelism that can be exploited through an efficient implementationusing various data storage devices, such as for example,single-instruction multiple-data (SIMD) vector registers. In SIMDexecution, a single instruction operates on multiple data elementsconcurrently or simultaneously. This is typically implemented byextending the width of various resources such as registers andarithmetic logic units (ALUs), allowing them to hold or operate onmultiple data elements, respectively.

The central processing unit (CPU) may provide such parallel hardware tosupport the SIMD processing of vectors. A vector is a data structurethat holds a number of consecutive data elements. A vector register ofsize L may contain N vector elements of size M, where N=L/M. Forinstance, a 64-byte vector register may be partitioned into (a) 64vector elements, with each element holding a data item that occupies 1byte, (b) 32 vector elements to hold data items that occupy 2 bytes (orone “word”) each, (c) 16 vector elements to hold data items that occupy4 bytes (or one “doubleword”) each, or (d) 8 vector elements to holddata items that occupy 8 bytes (or one “quadword”) each.

A number of applications have large amounts of data-level parallelismand may be able to benefit from SIMD support. To maintain SIMDefficiency, some architectures allow not only SIMD arithmetic operationsbut SIMD memory reads and writes and also SIMD shuffle and permutationoperations. However, some applications spend a significant amount oftime in operations on a set of sparse locations. Moreover, sometimessequential and/or conditional operations are performed and so theseapplications may see only limited benefit from having SIMD operations.

For example, the Princeton Application Repository for Shared-MemoryComputers (PARSEC) is a benchmark suite composed of multithreadedprograms. The suite focuses on emerging workloads and was designed to berepresentative of next-generation shared-memory programs forchip-multiprocessors. One of the PARSEC programs, streamcluster, solvesonline clustering problems by finding a predetermined number of mediansso that each point may be assigned to its nearest center. The programspends most of its time evaluating the gain of opening a new center. Theparallel gain computation is implemented in a function called, pgain,which includes the following loop:

  bool is_center[ ]; int center_table[ ]; int count = 0; for (int i =k1; i < k2; i++ ) {  if ( is_center[i] ) {   center_table[i] = count++; } }.

The example loop above illustrates conditional operations that areperformed on memory arrays, for which vectorization is difficult toachieve, and so limited benefit may be seen from processor architectureswhich allow SIMD operations.

To date, potential solutions to such performance limiting issues,sequential and/or conditional operations, and other bottlenecks have notbeen adequately explored.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings.

FIG. 1A is a block diagram of one embodiment of a system that executesinstructions to provide SIMD vectorization functionality for conditionalloops.

FIG. 1B is a block diagram of another embodiment of a system thatexecutes instructions to provide SIMD vectorization functionality forconditional loops.

FIG. 1C is a block diagram of another embodiment of a system thatexecutes instructions to provide SIMD vectorization functionality forconditional loops.

FIG. 2 is a block diagram of one embodiment of a processor that executesinstructions to provide SIMD vectorization functionality for conditionalloops.

FIG. 3A illustrates packed data types according to one embodiment.

FIG. 3B illustrates packed data types according to one embodiment.

FIG. 3C illustrates packed data types according to one embodiment.

FIG. 3D illustrates an instruction encoding to provide SIMDvectorization functionality for conditional loops according to oneembodiment.

FIG. 3E illustrates an instruction encoding to provide SIMDvectorization functionality for conditional loops according to anotherembodiment.

FIG. 3F illustrates an instruction encoding to provide SIMDvectorization functionality for conditional loops according to anotherembodiment.

FIG. 3G illustrates an instruction encoding to provide SIMDvectorization functionality for conditional loops according to anotherembodiment.

FIG. 3H illustrates an instruction encoding to provide SIMDvectorization functionality for conditional loops according to anotherembodiment.

FIG. 4A illustrates elements of one embodiment of a processormicro-architecture to execute instructions that provide SIMDvectorization functionality for conditional loops.

FIG. 4B illustrates elements of another embodiment of a processormicro-architecture to execute instructions that provide SIMDvectorization functionality for conditional loops.

FIG. 5 is a block diagram of one embodiment of a processor to executeinstructions that provide SIMD vectorization functionality forconditional loops.

FIG. 6 is a block diagram of one embodiment of a computer system toexecute instructions that provide SIMD vectorization functionality forconditional loops.

FIG. 7 is a block diagram of another embodiment of a computer system toexecute instructions that provide SIMD vectorization functionality forconditional loops.

FIG. 8 is a block diagram of another embodiment of a computer system toexecute instructions that provide SIMD vectorization functionality forconditional loops.

FIG. 9 is a block diagram of one embodiment of a system-on-a-chip toexecute instructions that provide SIMD vectorization functionality forconditional loops.

FIG. 10 is a block diagram of an embodiment of a processor to executeinstructions that provide SIMD vectorization functionality forconditional loops.

FIG. 11 is a block diagram of one embodiment of an IP core developmentsystem that provides SIMD vectorization functionality for conditionalloops.

FIG. 12 illustrates one embodiment of an architecture emulation systemthat provides SIMD vectorization functionality for conditional loops.

FIG. 13 illustrates one embodiment of a system to translate instructionsthat provide SIMD vectorization functionality for conditional loops.

FIG. 14A illustrates a flow diagram for one embodiment of a processusing an instruction to provide SIMD vectorization functionality forconditional loops.

FIG. 14B illustrates a flow diagram for another embodiment of a processusing an instruction to provide SIMD vectorization functionality forconditional loops.

FIG. 15A illustrates a flow diagram for one embodiment of a process ofexecuting a vector expand instruction to provide SIMD vectorizationfunctionality for conditional loops.

FIG. 15B illustrates a flow diagram for another embodiment of a processof executing a vector expand instruction to provide SIMD vectorizationfunctionality for conditional loops.

FIG. 16 illustrates a flow diagram for one embodiment of a process toprovide SIMD vectorization functionality for conditional loops.

FIG. 17 illustrates an embodiment of an apparatus for executing a vectorexpand instruction to provide SIMD vectorization functionality forconditional loops.

FIG. 18 illustrates another embodiment of an apparatus for executing avector expand instruction to provide SIMD vectorization functionalityfor conditional loops.

DETAILED DESCRIPTION

The following description discloses instructions and processing logic toprovide SIMD vectorization functionality for conditional loops within orin association with a processor, computer system, or other processingapparatus.

Instructions and logic are disclosed herein to provide vectorization ofconditional loops for a vector processor. A SIMD vector expandinstruction has a source parameter to specify a source vector, a maskparameter to specify a conditions mask register, and a destinationparameter to specify a destination vector to hold a plurality of nconsecutive vector elements, each of the plurality of n consecutivevector elements having a same variable partition size of m bytes. Inresponse to the processor instruction, data is copied from consecutivevector elements in the source vector, into unmasked vector elements ofthe specified destination vector, without copying data into maskedvector elements of the specified destination vector, wherein n variesresponsive to the processor instruction executed. Some embodiments storecounts of the condition decisions. Alternative embodiments may storeother data, for example such as target addresses, or table offsets, orindicators of processing directives, etc.

Some embodiments may set elements of a counts vector to n count values,e.g. such as consecutive count values. Then a portion of a memory array(e.g. is_center[i:i+n−1]) may be accessed to obtain a decisions vector.A SIMD vector comparison operation may then be used to generate a vectorconditions mask according to the decisions vector and the vectorconditions mask may be stored in a mask register. Responsive toexecuting the SIMD vector expand instruction, data is copied from theconsecutive vector elements in the source vector (e.g. consecutive countvalues) into unmasked vector elements of the destination vector, withoutcopying data from the source vector into masked vector elements of thedestination vector, wherein n varies responsive to the processorinstruction received (e.g. the variable partition size of m bytes may bethe size of integers in the array center_table[ ]). Then the data fromthe destination vector may be stored to memory according to a SIMDmasked vector write operation. Thus vectorization of the conditionalloop may be accomplished through the use of instructions and logicdisclosed herein in greater detail below.

An example of pseudo-code to perform the vectorized loop from thefunction, pgain, is shown below:

  vmm0 = {n−1, ... , 2, 1, 0}; for ( int i = k1; i < k2; i += n) {  vmm1= is_center[i: i+n−1];  mask = VCMPNEQ (vmm1, all_zeroes);  vmm2 =VEXPANDD (vmm0, mask);  center_table[i: i+n−1] = maskstore (vmm2, mask); vmm1 = broadcast (POPCNT(mask));  vmm0 = VADDD (vmm0, vmm1); }.

It will be appreciated that one embodiment to the vector expandinstruction may specify a destination vector in memory (e.g.center_table[i: i+n−1]) thereby eliminating the need for a separatemasked vector write (e.g. maskstore) operation. It will also beappreciated that vectorization of the conditional loop may beaccomplished through the use of instructions and logic disclosed herein,as shown in greater detail below, thereby increasing performance andinstruction throughput, and decreasing power use and energy consumption.These techniques may be employed in applications, such as onlineclustering where large amounts of continuously produced data need to beorganized under real-time conditions. Such applications may includenetwork intrusion detection, pattern recognition, and data mining, aswell as others.

In the following description, numerous specific details such asprocessing logic, processor types, micro-architectural conditions,events, enablement mechanisms, and the like are set forth in order toprovide a more thorough understanding of embodiments of the presentinvention. It will be appreciated, however, by one skilled in the artthat the invention may be practiced without such specific details.Additionally, some well known structures, circuits, and the like havenot been shown in detail to avoid unnecessarily obscuring embodiments ofthe present invention.

Although the following embodiments are described with reference to aprocessor, other embodiments are applicable to other types of integratedcircuits and logic devices. Similar techniques and teachings ofembodiments of the present invention can be applied to other types ofcircuits or semiconductor devices that can benefit from higher pipelinethroughput and improved performance. The teachings of embodiments of thepresent invention are applicable to any processor or machine thatperforms data manipulations. However, the present invention is notlimited to processors or machines that perform 512 bit, 256 bit, 128bit, 64 bit, 32 bit, or 16 bit data operations and can be applied to anyprocessor and machine in which manipulation or management of data isperformed. In addition, the following description provides examples, andthe accompanying drawings show various examples for the purposes ofillustration. However, these examples should not be construed in alimiting sense as they are merely intended to provide examples ofembodiments of the present invention rather than to provide anexhaustive list of all possible implementations of embodiments of thepresent invention.

Although the below examples describe instruction handling anddistribution in the context of execution units and logic circuits, otherembodiments of the present invention can be accomplished by way of dataand/or instructions stored on a machine-readable, tangible medium, whichwhen performed by a machine cause the machine to perform functionsconsistent with at least one embodiment of the invention. In oneembodiment, functions associated with embodiments of the presentinvention are embodied in machine-executable instructions. Theinstructions can be used to cause a general-purpose or special-purposeprocessor that is programmed with the instructions to perform the stepsof the present invention. Embodiments of the present invention may beprovided as a computer program product or software which may include amachine or computer-readable medium having stored thereon instructionswhich may be used to program a computer (or other electronic devices) toperform one or more operations according to embodiments of the presentinvention. Alternatively, steps of embodiments of the present inventionmight be performed by specific hardware components that containfixed-function logic for performing the steps, or by any combination ofprogrammed computer components and fixed-function hardware components.

Instructions used to program logic to perform embodiments of theinvention can be stored within a memory in the system, such as DRAM,cache, flash memory, or other storage. Furthermore, the instructions canbe distributed via a network or by way of other computer readable media.Thus a machine-readable medium may include any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer), but is not limited to, floppy diskettes, optical disks,Compact Disc, Read-Only Memory (CD-ROMs), and magneto-optical disks,Read-Only Memory (ROMs), Random Access Memory (RAM), ErasableProgrammable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM), magnetic or optical cards, flashmemory, or a tangible, machine-readable storage used in the transmissionof information over the Internet via electrical, optical, acoustical orother forms of propagated signals (e.g., carrier waves, infraredsignals, digital signals, etc.). Accordingly, the computer-readablemedium includes any type of tangible machine-readable medium suitablefor storing or transmitting electronic instructions or information in aform readable by a machine (e.g., a computer).

A design may go through various stages, from creation to simulation tofabrication. Data representing a design may represent the design in anumber of manners. First, as is useful in simulations, the hardware maybe represented using a hardware description language or anotherfunctional description language. Additionally, a circuit level modelwith logic and/or transistor gates may be produced at some stages of thedesign process. Furthermore, most designs, at some stage, reach a levelof data representing the physical placement of various devices in thehardware model. In the case where conventional semiconductor fabricationtechniques are used, the data representing the hardware model may be thedata specifying the presence or absence of various features on differentmask layers for masks used to produce the integrated circuit. In anyrepresentation of the design, the data may be stored in any form of amachine readable medium. A memory or a magnetic or optical storage suchas a disc may be the machine readable medium to store informationtransmitted via optical or electrical wave modulated or otherwisegenerated to transmit such information. When an electrical carrier waveindicating or carrying the code or design is transmitted, to the extentthat copying, buffering, or re-transmission of the electrical signal isperformed, a new copy is made. Thus, a communication provider or anetwork provider may store on a tangible, machine-readable medium, atleast temporarily, an article, such as information encoded into acarrier wave, embodying techniques of embodiments of the presentinvention.

In modern processors, a number of different execution units are used toprocess and execute a variety of code and instructions. Not allinstructions are created equal as some are quicker to complete whileothers can take a number of clock cycles to complete. The faster thethroughput of instructions, the better the overall performance of theprocessor. Thus it would be advantageous to have as many instructionsexecute as fast as possible. However, there are certain instructionsthat have greater complexity and require more in terms of execution timeand processor resources. For example, there are floating pointinstructions, load/store operations, data moves, etc.

As more computer systems are used in internet, text, and multimediaapplications, additional processor support has been introduced overtime. In one embodiment, an instruction set may be associated with oneor more computer architectures, including data types, instructions,register architecture, addressing modes, memory architecture, interruptand exception handling, and external input and output (I/O).

In one embodiment, the instruction set architecture (ISA) may beimplemented by one or more micro-architectures, which includes processorlogic and circuits used to implement one or more instruction sets.Accordingly, processors with different micro-architectures can share atleast a portion of a common instruction set. For example, Intel® Pentium4 processors, Intel® Core™ processors, and processors from AdvancedMicro Devices, Inc. of Sunnyvale Calif. implement nearly identicalversions of the x86 instruction set (with some extensions that have beenadded with newer versions), but have different internal designs.Similarly, processors designed by other processor development companies,such as ARM Holdings, Ltd., MIPS, or their licensees or adopters, mayshare at least a portion a common instruction set, but may includedifferent processor designs. For example, the same register architectureof the ISA may be implemented in different ways in differentmicro-architectures using new or well-known techniques, includingdedicated physical registers, one or more dynamically allocated physicalregisters using a register renaming mechanism (e.g., the use of aRegister Alias Table (RAT), a Reorder Buffer (ROB) and a retirementregister file. In one embodiment, registers may include one or moreregisters, register architectures, register files, or other registersets that may or may not be addressable by a software programmer.

In one embodiment, an instruction may include one or more instructionformats. In one embodiment, an instruction format may indicate variousfields (number of bits, location of bits, etc.) to specify, among otherthings, the operation to be performed and the operand(s) on which thatoperation is to be performed. Some instruction formats may be furtherbroken defined by instruction templates (or sub formats). For example,the instruction templates of a given instruction format may be definedto have different subsets of the instruction format's fields and/ordefined to have a given field interpreted differently. In oneembodiment, an instruction is expressed using an instruction format(and, if defined, in a given one of the instruction templates of thatinstruction format) and specifies or indicates the operation and theoperands upon which the operation will operate.

Scientific, financial, auto-vectorized general purpose, RMS(recognition, mining, and synthesis), and visual and multimediaapplications (e.g., 2D/3D graphics, image processing, videocompression/decompression, voice recognition algorithms and audiomanipulation) may require the same operation to be performed on a largenumber of data items. In one embodiment, Single Instruction MultipleData (SIMD) refers to a type of instruction that causes a processor toperform an operation on multiple data elements. SIMD technology may beused in processors that can logically divide the bits in a register intoa number of fixed-sized or variable-sized data elements, each of whichrepresents a separate value. For example, in one embodiment, the bits ina 64-bit register may be organized as a source operand containing fourseparate 16-bit data elements, each of which represents a separate16-bit value. This type of data may be referred to as ‘packed’ data typeor ‘vector’ data type, and operands of this data type are referred to aspacked data operands or vector operands. In one embodiment, a packeddata item or vector may be a sequence of packed data elements storedwithin a single register, and a packed data operand or a vector operandmay a source or destination operand of a SIMD instruction (or ‘packeddata instruction’ or a ‘vector instruction’). In one embodiment, a SIMDinstruction specifies a single vector operation to be performed on twosource vector operands to generate a destination vector operand (alsoreferred to as a result vector operand) of the same or different size,with the same or different number of data elements, and in the same ordifferent data element order.

SIMD technology, such as that employed by the Intel® Core™ processorshaving an instruction set including x86, MMX™, Streaming SIMD Extensions(SSE), SSE2, SSE3, SSE4.1, and SSE4.2 instructions, ARM processors, suchas the ARM Cortex® family of processors having an instruction setincluding the Vector Floating Point (VFP) and/or NEON instructions, andMIPS processors, such as the Loongson family of processors developed bythe Institute of Computing Technology (ICT) of the Chinese Academy ofSciences, has enabled a significant improvement in applicationperformance (Core™ and MMX™ are registered trademarks or trademarks ofIntel Corporation of Santa Clara, Calif.).

In one embodiment, destination and source registers/data are genericterms to represent the source and destination of the corresponding dataor operation. In some embodiments, they may be implemented by registers,memory, or other storage areas having other names or functions thanthose depicted. For example, in one embodiment, “DEST1” may be atemporary storage register or other storage area, whereas “SRC1” and“SRC2” may be a first and second source storage register or otherstorage area, and so forth. In other embodiments, two or more of the SRCand DEST storage areas may correspond to different data storage elementswithin the same storage area (e.g., a SIMD register). In one embodiment,one of the source registers may also act as a destination register by,for example, writing back the result of an operation performed on thefirst and second source data to one of the two source registers servingas a destination registers.

FIG. 1A is a block diagram of an exemplary computer system formed with aprocessor that includes execution units to execute an instruction inaccordance with one embodiment of the present invention. System 100includes a component, such as a processor 102 to employ execution unitsincluding logic to perform algorithms for process data, in accordancewith the present invention, such as in the embodiment described herein.System 100 is representative of processing systems based on the PENTIUM®III, PENTIUM® 4, Xeon™, Itanium®, XScale™ and/or StrongARM™microprocessors available from Intel Corporation of Santa Clara, Calif.,although other systems (including PCs having other microprocessors,engineering workstations, set-top boxes and the like) may also be used.In one embodiment, sample system 100 may execute a version of theWINDOWS™ operating system available from Microsoft Corporation ofRedmond, Wash., although other operating systems (UNIX and Linux forexample), embedded software, and/or graphical user interfaces, may alsobe used. Thus, embodiments of the present invention are not limited toany specific combination of hardware circuitry and software.

Embodiments are not limited to computer systems. Alternative embodimentsof the present invention can be used in other devices such as handhelddevices and embedded applications. Some examples of handheld devicesinclude cellular phones, Internet Protocol devices, digital cameras,personal digital assistants (PDAs), and handheld PCs. Embeddedapplications can include a micro controller, a digital signal processor(DSP), system on a chip, network computers (NetPC), set-top boxes,network hubs, wide area network (WAN) switches, or any other system thatcan perform one or more instructions in accordance with at least oneembodiment.

FIG. 1A is a block diagram of a computer system 100 formed with aprocessor 102 that includes one or more execution units 108 to performan algorithm to perform at least one instruction in accordance with oneembodiment of the present invention. One embodiment may be described inthe context of a single processor desktop or server system, butalternative embodiments can be included in a multiprocessor system.System 100 is an example of a ‘hub’ system architecture. The computersystem 100 includes a processor 102 to process data signals. Theprocessor 102 can be a complex instruction set computer (CISC)microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, aprocessor implementing a combination of instruction sets, or any otherprocessor device, such as a digital signal processor, for example. Theprocessor 102 is coupled to a processor bus 110 that can transmit datasignals between the processor 102 and other components in the system100. The elements of system 100 perform their conventional functionsthat are well known to those familiar with the art.

In one embodiment, the processor 102 includes a Level 1 (L1) internalcache memory 104. Depending on the architecture, the processor 102 canhave a single internal cache or multiple levels of internal cache.Alternatively, in another embodiment, the cache memory can resideexternal to the processor 102. Other embodiments can also include acombination of both internal and external caches depending on theparticular implementation and needs. Register file 106 can storedifferent types of data in various registers including integerregisters, floating point registers, status registers, and instructionpointer register.

Execution unit 108, including logic to perform integer and floatingpoint operations, also resides in the processor 102. The processor 102also includes a microcode (ucode) ROM that stores microcode for certainmacroinstructions. For one embodiment, execution unit 108 includes logicto handle a packed instruction set 109. By including the packedinstruction set 109 in the instruction set of a general-purposeprocessor 102, along with associated circuitry to execute theinstructions, the operations used by many multimedia applications may beperformed using packed data in a general-purpose processor 102. Thus,many multimedia applications can be accelerated and executed moreefficiently by using the full width of a processor's data bus forperforming operations on packed data. This can eliminate the need totransfer smaller units of data across the processor's data bus toperform one or more operations one data element at a time.

Alternate embodiments of an execution unit 108 can also be used in microcontrollers, embedded processors, graphics devices, DSPs, and othertypes of logic circuits. System 100 includes a memory 120. Memory 120can be a dynamic random access memory (DRAM) device, a static randomaccess memory (SRAM) device, flash memory device, or other memorydevice. Memory 120 can store instructions and/or data represented bydata signals that can be executed by the processor 102.

A system logic chip 116 is coupled to the processor bus 110 and memory120. The system logic chip 116 in the illustrated embodiment is a memorycontroller hub (MCH). The processor 102 can communicate to the MCH 116via a processor bus 110. The MCH 116 provides a high bandwidth memorypath 118 to memory 120 for instruction and data storage and for storageof graphics commands, data and textures. The MCH 116 is to direct datasignals between the processor 102, memory 120, and other components inthe system 100 and to bridge the data signals between processor bus 110,memory 120, and system I/O 122. In some embodiments, the system logicchip 116 can provide a graphics port for coupling to a graphicscontroller 112. The MCH 116 is coupled to memory 120 through a memoryinterface 118. The graphics card 112 is coupled to the MCH 116 throughan Accelerated Graphics Port (AGP) interconnect 114.

System 100 uses a proprietary hub interface bus 122 to couple the MCH116 to the I/O controller hub (ICH) 130. The ICH 130 provides directconnections to some I/O devices via a local I/O bus. The local I/O busis a high-speed I/O bus for connecting peripherals to the memory 120,chipset, and processor 102. Some examples are the audio controller,firmware hub (flash BIOS) 128, wireless transceiver 126, data storage124, legacy I/O controller containing user input and keyboardinterfaces, a serial expansion port such as Universal Serial Bus (USB),and a network controller 134. The data storage device 124 can comprise ahard disk drive, a floppy disk drive, a CD-ROM device, a flash memorydevice, or other mass storage device.

For another embodiment of a system, an instruction in accordance withone embodiment can be used with a system on a chip. One embodiment of asystem on a chip comprises of a processor and a memory. The memory forone such system is a flash memory. The flash memory can be located onthe same die as the processor and other system components. Additionally,other logic blocks such as a memory controller or graphics controllercan also be located on a system on a chip.

FIG. 1B illustrates a data processing system 140 which implements theprinciples of one embodiment of the present invention. It will bereadily appreciated by one of skill in the art that the embodimentsdescribed herein can be used with alternative processing systems withoutdeparture from the scope of embodiments of the invention.

Computer system 140 comprises a processing core 159 capable ofperforming at least one instruction in accordance with one embodiment.For one embodiment, processing core 159 represents a processing unit ofany type of architecture, including but not limited to a CISC, a RISC ora VLIW type architecture. Processing core 159 may also be suitable formanufacture in one or more process technologies and by being representedon a machine readable media in sufficient detail, may be suitable tofacilitate said manufacture.

Processing core 159 comprises an execution unit 142, a set of registerfile(s) 145, and a decoder 144. Processing core 159 also includesadditional circuitry (not shown) which is not necessary to theunderstanding of embodiments of the present invention. Execution unit142 is used for executing instructions received by processing core 159.In addition to performing typical processor instructions, execution unit142 can perform instructions in packed instruction set 143 forperforming operations on packed data formats. Packed instruction set 143includes instructions for performing embodiments of the invention andother packed instructions. Execution unit 142 is coupled to registerfile 145 by an internal bus. Register file 145 represents a storage areaon processing core 159 for storing information, including data. Aspreviously mentioned, it is understood that the storage area used forstoring the packed data is not critical. Execution unit 142 is coupledto decoder 144. Decoder 144 is used for decoding instructions receivedby processing core 159 into control signals and/or microcode entrypoints. In response to these control signals and/or microcode entrypoints, execution unit 142 performs the appropriate operations. In oneembodiment, the decoder is used to interpret the opcode of theinstruction, which will indicate what operation should be performed onthe corresponding data indicated within the instruction.

Processing core 159 is coupled with bus 141 for communicating withvarious other system devices, which may include but are not limited to,for example, synchronous dynamic random access memory (SDRAM) control146, static random access memory (SRAM) control 147, burst flash memoryinterface 148, personal computer memory card international association(PCMCIA)/compact flash (CF) card control 149, liquid crystal display(LCD) control 150, direct memory access (DMA) controller 151, andalternative bus master interface 152. In one embodiment, data processingsystem 140 may also comprise an I/O bridge 154 for communicating withvarious I/O devices via an I/O bus 153. Such I/O devices may include butare not limited to, for example, universal asynchronousreceiver/transmitter (UART) 155, universal serial bus (USB) 156,Bluetooth wireless UART 157 and I/O expansion interface 158.

One embodiment of data processing system 140 provides for mobile,network and/or wireless communications and a processing core 159 capableof performing SIMD operations including a text string comparisonoperation. Processing core 159 may be programmed with various audio,video, imaging and communications algorithms including discretetransformations such as a Walsh-Hadamard transform, a fast Fouriertransform (FFT), a discrete cosine transform (DCT), and their respectiveinverse transforms; compression/decompression techniques such as colorspace transformation, video encode motion estimation or video decodemotion compensation; and modulation/demodulation (MODEM) functions suchas pulse coded modulation (PCM).

FIG. 1C illustrates another alternative embodiments of a data processingsystem capable of executing instructions to provide SIMD vectorizationfunctionality for conditional loops. In accordance with one alternativeembodiment, data processing system 160 may include a main processor 166,a SIMD coprocessor 161, a cache memory 167, and an input/output system168. The input/output system 168 may optionally be coupled to a wirelessinterface 169. SIMD coprocessor 161 is capable of performing operationsincluding instructions in accordance with one embodiment. Processingcore 170 may be suitable for manufacture in one or more processtechnologies and by being represented on a machine readable media insufficient detail, may be suitable to facilitate the manufacture of allor part of data processing system 160 including processing core 170.

For one embodiment, SIMD coprocessor 161 comprises an execution unit 162and a set of register file(s) 164. One embodiment of main processor 166comprises a decoder 165 to recognize instructions of instruction set 163including instructions in accordance with one embodiment for executionby execution unit 162. For alternative embodiments, SIMD coprocessor 161also comprises at least part of decoder 165B to decode instructions ofinstruction set 163. Processing core 170 also includes additionalcircuitry (not shown) which is not necessary to the understanding ofembodiments of the present invention.

In operation, the main processor 166 executes a stream of dataprocessing instructions that control data processing operations of ageneral type including interactions with the cache memory 167, and theinput/output system 168. Embedded within the stream of data processinginstructions are SIMD coprocessor instructions. The decoder 165 of mainprocessor 166 recognizes these SIMD coprocessor instructions as being ofa type that should be executed by an attached SIMD coprocessor 161.Accordingly, the main processor 166 issues these SIMD coprocessorinstructions (or control signals representing SIMD coprocessorinstructions) on the coprocessor bus 171 where from they are received byany attached SIMD coprocessors. In this case, the SIMD coprocessor 161will accept and execute any received SIMD coprocessor instructionsintended for it.

Data may be received via wireless interface 169 for processing by theSIMD coprocessor instructions. For one example, voice communication maybe received in the form of a digital signal, which may be processed bythe SIMD coprocessor instructions to regenerate digital audio samplesrepresentative of the voice communications. For another example,compressed audio and/or video may be received in the form of a digitalbit stream, which may be processed by the SIMD coprocessor instructionsto regenerate digital audio samples and/or motion video frames. For oneembodiment of processing core 170, main processor 166, and a SIMDcoprocessor 161 are integrated into a single processing core 170comprising an execution unit 162, a set of register file(s) 164, and adecoder 165 to recognize instructions of instruction set 163 includinginstructions in accordance with one embodiment.

FIG. 2 is a block diagram of the micro-architecture for a processor 200that includes logic circuits to perform instructions in accordance withone embodiment of the present invention. In some embodiments, aninstruction in accordance with one embodiment can be implemented tooperate on data elements having sizes of byte, word, doubleword,quadword, etc., as well as datatypes, such as single and doubleprecision integer and floating point datatypes. In one embodiment thein-order front end 201 is the part of the processor 200 that fetchesinstructions to be executed and prepares them to be used later in theprocessor pipeline. The front end 201 may include several units. In oneembodiment, the instruction prefetcher 226 fetches instructions frommemory and feeds them to an instruction decoder 228 which in turndecodes or interprets them. For example, in one embodiment, the decoderdecodes a received instruction into one or more operations called“micro-instructions” or “micro-operations” (also called micro op oruops) that the machine can execute. In other embodiments, the decoderparses the instruction into an opcode and corresponding data and controlfields that are used by the micro-architecture to perform operations inaccordance with one embodiment. In one embodiment, the trace cache 230takes decoded uops and assembles them into program ordered sequences ortraces in the uop queue 234 for execution. When the trace cache 230encounters a complex instruction, the microcode ROM 232 provides theuops needed to complete the operation.

Some instructions are converted into a single micro-op, whereas othersneed several micro-ops to complete the full operation. In oneembodiment, if more than four micro-ops are needed to complete ainstruction, the decoder 228 accesses the microcode ROM 232 to do theinstruction. For one embodiment, an instruction can be decoded into asmall number of micro ops for processing at the instruction decoder 228.In another embodiment, an instruction can be stored within the microcodeROM 232 should a number of micro-ops be needed to accomplish theoperation. The trace cache 230 refers to a entry point programmablelogic array (PLA) to determine a correct micro-instruction pointer forreading the micro-code sequences to complete one or more instructions inaccordance with one embodiment from the micro-code ROM 232. After themicrocode ROM 232 finishes sequencing micro-ops for an instruction, thefront end 201 of the machine resumes fetching micro-ops from the tracecache 230.

The out-of-order execution engine 203 is where the instructions areprepared for execution. The out-of-order execution logic has a number ofbuffers to smooth out and re-order the flow of instructions to optimizeperformance as they go down the pipeline and get scheduled forexecution. The allocator logic allocates the machine buffers andresources that each uop needs in order to execute. The register renaminglogic renames logic registers onto entries in a register file. Theallocator also allocates an entry for each uop in one of the two uopqueues, one for memory operations and one for non-memory operations, infront of the instruction schedulers: memory scheduler, fast scheduler202, slow/general floating point scheduler 204, and simple floatingpoint scheduler 206. The uop schedulers 202, 204, 206, determine when auop is ready to execute based on the readiness of their dependent inputregister operand sources and the availability of the execution resourcesthe uops need to complete their operation. The fast scheduler 202 of oneembodiment can schedule on each half of the main clock cycle while theother schedulers can only schedule once per main processor clock cycle.The schedulers arbitrate for the dispatch ports to schedule uops forexecution.

Register files 208, 210, sit between the schedulers 202, 204, 206, andthe execution units 212, 214, 216, 218, 220, 222, 224 in the executionblock 211. There is a separate register file 208, 210, for integer andfloating point operations, respectively. Each register file 208, 210, ofone embodiment also includes a bypass network that can bypass or forwardjust completed results that have not yet been written into the registerfile to new dependent uops. The integer register file 208 and thefloating point register file 210 are also capable of communicating datawith the other. For one embodiment, the integer register file 208 issplit into two separate register files, one register file for the loworder 32 bits of data and a second register file for the high order 32bits of data. The floating point register file 210 of one embodiment has128 bit wide entries because floating point instructions typically haveoperands from 64 to 128 bits in width.

The execution block 211 contains the execution units 212, 214, 216, 218,220, 222, 224, where the instructions are actually executed. Thissection includes the register files 208, 210, that store the integer andfloating point data operand values that the micro-instructions need toexecute. The processor 200 of one embodiment is comprised of a number ofexecution units: address generation unit (AGU) 212, AGU 214, fast ALU216, fast ALU 218, slow ALU 220, floating point ALU 222, floating pointmove unit 224. For one embodiment, the floating point execution blocks222, 224, execute floating point, MMX, SIMD, and SSE, or otheroperations. The floating point ALU 222 of one embodiment includes a 64bit by 64 bit floating point divider to execute divide, square root, andremainder micro-ops. For embodiments of the present invention,instructions involving a floating point value may be handled with thefloating point hardware. In one embodiment, the ALU operations go to thehigh-speed ALU execution units 216, 218. The fast ALUs 216, 218, of oneembodiment can execute fast operations with an effective latency of halfa clock cycle. For one embodiment, most complex integer operations go tothe slow ALU 220 as the slow ALU 220 includes integer execution hardwarefor long latency type of operations, such as a multiplier, shifts, flaglogic, and branch processing. Memory load/store operations are executedby the AGUs 212, 214. For one embodiment, the integer ALUs 216, 218,220, are described in the context of performing integer operations on 64bit data operands. In alternative embodiments, the ALUs 216, 218, 220,can be implemented to support a variety of data bits including 16, 32,128, 256, etc. Similarly, the floating point units 222, 224, can beimplemented to support a range of operands having bits of variouswidths. For one embodiment, the floating point units 222, 224, canoperate on 128 bits wide packed data operands in conjunction with SIMDand multimedia instructions.

In one embodiment, the uops schedulers 202, 204, 206, dispatch dependentoperations before the parent load has finished executing. As uops arespeculatively scheduled and executed in processor 200, the processor 200also includes logic to handle memory misses. If a data load misses inthe data cache, there can be dependent operations in flight in thepipeline that have left the scheduler with temporarily incorrect data. Areplay mechanism tracks and re-executes instructions that use incorrectdata. Only the dependent operations need to be replayed and theindependent ones are allowed to complete. The schedulers and replaymechanism of one embodiment of a processor are also designed to catchinstructions that provide SIMD vectorization functionality forconditional loops.

The term “registers” may refer to the on-board processor storagelocations that are used as part of instructions to identify operands. Inother words, registers may be those that are usable from the outside ofthe processor (from a programmer's perspective). However, the registersof an embodiment should not be limited in meaning to a particular typeof circuit. Rather, a register of an embodiment is capable of storingand providing data, and performing the functions described herein. Theregisters described herein can be implemented by circuitry within aprocessor using any number of different techniques, such as dedicatedphysical registers, dynamically allocated physical registers usingregister renaming, combinations of dedicated and dynamically allocatedphysical registers, etc. In one embodiment, integer registers storethirty-two bit integer data. A register file of one embodiment alsocontains eight multimedia SIMD registers for packed data. For thediscussions below, the registers are understood to be data registersdesigned to hold packed data, such as 64 bits wide MMX™ registers (alsoreferred to as ‘mm’ registers in some instances) in microprocessorsenabled with MMX technology from Intel Corporation of Santa Clara,Calif. These MMX registers, available in both integer and floating pointforms, can operate with packed data elements that accompany SIMD and SSEinstructions. Similarly, 128 bits wide XMM registers relating to SSE2,SSE3, SSE4, or beyond (referred to generically as “SSEx”) technology canalso be used to hold such packed data operands. In one embodiment, instoring packed data and integer data, the registers do not need todifferentiate between the two data types. In one embodiment, integer andfloating point are either contained in the same register file ordifferent register files. Furthermore, in one embodiment, floating pointand integer data may be stored in different registers or the sameregisters.

In the examples of the following figures, a number of data operands aredescribed. FIG. 3A illustrates various packed data type representationsin multimedia registers according to one embodiment of the presentinvention. FIG. 3A illustrates data types for a packed byte 310, apacked word 320, and a packed doubleword (dword) 330 for 128 bits wideoperands. The packed byte format 310 of this example is 128 bits longand contains sixteen packed byte data elements. A byte is defined hereas 8 bits of data. Information for each byte data element is stored inbit 7 through bit 0 for byte 0, bit 15 through bit 8 for byte 1, bit 23through bit 16 for byte 2, and finally bit 120 through bit 127 for byte15. Thus, all available bits are used in the register. This storagearrangement increases the storage efficiency of the processor. As well,with sixteen data elements accessed, one operation can now be performedon sixteen data elements in parallel.

Generally, a data element is an individual piece of data that is storedin a single register or memory location with other data elements of thesame length. In packed data sequences relating to SSEx technology, thenumber of data elements stored in a XMM register is 128 bits divided bythe length in bits of an individual data element. Similarly, in packeddata sequences relating to MMX and SSE technology, the number of dataelements stored in an MMX register is 64 bits divided by the length inbits of an individual data element. Although the data types illustratedin FIG. 3A are 128 bit long, embodiments of the present invention canalso operate with 64 bit wide, 256 bit wide, 512 bit wide, or othersized operands. The packed word format 320 of this example is 128 bitslong and contains eight packed word data elements. Each packed wordcontains sixteen bits of information. The packed doubleword format 330of FIG. 3A is 128 bits long and contains four packed doubleword dataelements. Each packed doubleword data element contains thirty two bitsof information. A packed quadword is 128 bits long and contains twopacked quad-word data elements.

FIG. 3B illustrates alternative in-register data storage formats. Eachpacked data can include more than one independent data element. Threepacked data formats are illustrated; packed half 341, packed single 342,and packed double 343. One embodiment of packed half 341, packed single342, and packed double 343 contain fixed-point data elements. For analternative embodiment one or more of packed half 341, packed single342, and packed double 343 may contain floating-point data elements. Onealternative embodiment of packed half 341 is one hundred twenty-eightbits long containing eight 16-bit data elements. One embodiment ofpacked single 342 is one hundred twenty-eight bits long and containsfour 32-bit data elements. One embodiment of packed double 343 is onehundred twenty-eight bits long and contains two 64-bit data elements. Itwill be appreciated that such packed data formats may be furtherextended to other register lengths, for example, to 96-bits, 160-bits,192-bits, 224-bits, 256-bits, 512-bits or more.

FIG. 3C illustrates various signed and unsigned packed data typerepresentations in multimedia registers according to one embodiment ofthe present invention. Unsigned packed byte representation 344illustrates the storage of an unsigned packed byte in a SIMD register.Information for each byte data element is stored in bit seven throughbit zero for byte zero, bit fifteen through bit eight for byte one, bittwenty-three through bit sixteen for byte two, etc., and finally bit onehundred twenty through bit one hundred twenty-seven for byte fifteen.Thus, all available bits are used in the register. This storagearrangement can increase the storage efficiency of the processor. Aswell, with sixteen data elements accessed, one operation can now beperformed on sixteen data elements in a parallel fashion. Signed packedbyte representation 345 illustrates the storage of a signed packed byte.Note that the eighth bit of every byte data element is the signindicator. Unsigned packed word representation 346 illustrates how wordseven through word zero are stored in a SIMD register. Signed packedword representation 347 is similar to the unsigned packed wordin-register representation 346. Note that the sixteenth bit of each worddata element is the sign indicator. Unsigned packed doublewordrepresentation 348 shows how doubleword data elements are stored. Signedpacked doubleword representation 349 is similar to unsigned packeddoubleword in-register representation 348. Note that the necessary signbit is the thirty-second bit of each doubleword data element.

FIG. 3D is a depiction of one embodiment of an operation encoding(opcode) format 360, having thirty-two or more bits, and register/memoryoperand addressing modes corresponding with a type of opcode formatdescribed in the “Intel® 64 and IA-32 Intel Architecture SoftwareDeveloper's Manual Combined Volumes 2A and 2B: Instruction Set ReferenceA-Z,” which is which is available from Intel Corporation, Santa Clara,Calif. on the world-wide-web (www) atintel.com/products/processor/manuals/. In one embodiment, andinstruction may be encoded by one or more of fields 361 and 362. Up totwo operand locations per instruction may be identified, including up totwo source operand identifiers 364 and 365. For one embodiment,destination operand identifier 366 is the same as source operandidentifier 364, whereas in other embodiments they are different. For analternative embodiment, destination operand identifier 366 is the sameas source operand identifier 365, whereas in other embodiments they aredifferent. In one embodiment, one of the source operands identified bysource operand identifiers 364 and 365 is overwritten by the results ofthe instruction, whereas in other embodiments identifier 364 correspondsto a source register element and identifier 365 corresponds to adestination register element. For one embodiment, operand identifiers364 and 365 may be used to identify 32-bit or 64-bit source anddestination operands.

FIG. 3E is a depiction of another alternative operation encoding(opcode) format 370, having forty or more bits. Opcode format 370corresponds with opcode format 360 and comprises an optional prefix byte378. An instruction according to one embodiment may be encoded by one ormore of fields 378, 371, and 372. Up to two operand locations perinstruction may be identified by source operand identifiers 374 and 375and by prefix byte 378. For one embodiment, prefix byte 378 may be usedto identify 32-bit or 64-bit source and destination operands. For oneembodiment, destination operand identifier 376 is the same as sourceoperand identifier 374, whereas in other embodiments they are different.For an alternative embodiment, destination operand identifier 376 is thesame as source operand identifier 375, whereas in other embodiments theyare different. In one embodiment, an instruction operates on one or moreof the operands identified by operand identifiers 374 and 375 and one ormore operands identified by the operand identifiers 374 and 375 isoverwritten by the results of the instruction, whereas in otherembodiments, operands identified by identifiers 374 and 375 are writtento another data element in another register. Opcode formats 360 and 370allow register to register, memory to register, register by memory,register by register, register by immediate, register to memoryaddressing specified in part by MOD fields 363 and 373 and by optionalscale-index-base and displacement bytes.

Turning next to FIG. 3F, in some alternative embodiments, 64-bit (or128-bit, or 256-bit, or 512-bit or more) single instruction multipledata (SIMD) arithmetic operations may be performed through a coprocessordata processing (CDP) instruction. Operation encoding (opcode) format380 depicts one such CDP instruction having CDP opcode fields 382 and389. The type of CDP instruction, for alternative embodiments,operations may be encoded by one or more of fields 383, 384, 387, and388. Up to three operand locations per instruction may be identified,including up to two source operand identifiers 385 and 390 and onedestination operand identifier 386. One embodiment of the coprocessorcan operate on 8, 16, 32, and 64 bit values. For one embodiment, aninstruction is performed on integer data elements. In some embodiments,an instruction may be executed conditionally, using condition field 381.For some embodiments, source data sizes may be encoded by field 383. Insome embodiments, Zero (Z), negative (N), carry (C), and overflow (V)detection can be done on SIMD fields. For some instructions, the type ofsaturation may be encoded by field 384.

Turning next to FIG. 3G is a depiction of another alternative operationencoding (opcode) format 397, to provide SIMD vectorizationfunctionality for conditional loops according to another embodiment,corresponding with a type of opcode format described in the “Intel®Advanced Vector Extensions Programming Reference,” which is availablefrom Intel Corp., Santa Clara, Calif. on the world-wide-web (www) atintel.com/products/processor/manuals/.

The original x86 instruction set provided for a 1-byte opcode withvarious formats of address syllable and immediate operand contained inadditional bytes whose presence was known from the first “opcode” byte.Additionally, there were certain byte values that were reserved asmodifiers to the opcode (called prefixes, as they had to be placedbefore the instruction). When the original palette of 256 opcode bytes(including these special prefix values) was exhausted, a single byte wasdedicated as an escape to a new set of 256 opcodes. As vectorinstructions (e.g., SIMD) were added, a need for more opcodes wasgenerated, and the “two byte” opcode map also was insufficient, evenwhen expanded through the use of prefixes. To this end, new instructionswere added in additional maps which use 2 bytes plus an optional prefixas an identifier.

Additionally, in order to facilitate additional registers in 64-bitmode, an additional prefix may be used (called “REX”) in between theprefixes and the opcode (and any escape bytes necessary to determine theopcode). In one embodiment, the REX may have 4 “payload” bits toindicate use of additional registers in 64-bit mode. In otherembodiments it may have fewer or more than 4 bits. The general format ofat least one instruction set (which corresponds generally with format360 and/or format 370) is illustrated generically by the following:

-   -   [prefixes] [rex] escape [escape2] opcode modrm (etc.)

Opcode format 397 corresponds with opcode format 370 and comprisesoptional VEX prefix bytes 391 (beginning with C4 hex in one embodiment)to replace most other commonly used legacy instruction prefix bytes andescape codes. For example, the following illustrates an embodiment usingtwo fields to encode an instruction, which may be used when a secondescape code is present in the original instruction, or when extra bits(e.g, the XB and W fields) in the REX field need to be used. In theembodiment illustrated below, legacy escape is represented by a newescape value, legacy prefixes are fully compressed as part of the“payload” bytes, legacy prefixes are reclaimed and available for futureexpansion, the second escape code is compressed in a “map” field, withfuture map or feature space available, and new features are added (e.g.,increased vector length and an additional source register specifier).

An instruction according to one embodiment may be encoded by one or moreof fields 391 and 392. Up to four operand locations per instruction maybe identified by field 391 in combination with source operandidentifiers 374 and 375 and in combination with an optionalscale-index-base (SIB) identifier 393, an optional displacementidentifier 394, and an optional immediate byte 395. For one embodiment,VEX prefix bytes 391 may be used to identify 32-bit or 64-bit source anddestination operands and/or 128-bit or 256-bit SIMD register or memoryoperands. For one embodiment, the functionality provided by opcodeformat 397 may be redundant with opcode format 370, whereas in otherembodiments they are different. Opcode formats 370 and 397 allowregister to register, memory to register, register by memory, registerby register, register by immediate, register to memory addressingspecified in part by MOD field 373 and by optional (SIB) identifier 393,an optional displacement identifier 394, and an optional immediate byte395.

Turning next to FIG. 3H is a depiction of another alternative operationencoding (opcode) format 398, to provide SIMD vectorizationfunctionality for conditional loops according to another embodiment.Opcode format 398 corresponds with opcode formats 370 and 397 andcomprises optional EVEX prefix bytes 396 (beginning with 62 hex in oneembodiment) to replace most other commonly used legacy instructionprefix bytes and escape codes and provide additional functionality. Aninstruction according to one embodiment may be encoded by one or more offields 396 and 392. Up to four operand locations per instruction and amask may be identified by field 396 in combination with source operandidentifiers 374 and 375 and in combination with an optionalscale-index-base (SIB) identifier 393, an optional displacementidentifier 394, and an optional immediate byte 395. For one embodiment,EVEX prefix bytes 396 may be used to identify 32-bit or 64-bit sourceand destination operands and/or 128-bit, 256-bit or 512-bit SIMDregister or memory operands. For one embodiment, the functionalityprovided by opcode format 398 may be redundant with opcode formats 370or 397, whereas in other embodiments they are different. Opcode format398 allows register to register, memory to register, register by memory,register by register, register by immediate, register to memoryaddressing, with masks, specified in part by MOD field 373 and byoptional (SIB) identifier 393, an optional displacement identifier 394,and an optional immediate byte 395. The general format of at least oneinstruction set (which corresponds generally with format 360 and/orformat 370) is illustrated generically by the following:

-   -   evex1 RXBmmmmm WvvvLpp evex4 opcode modrm [sib] [disp] [imm]

For one embodiment an instruction encoded according to the EVEX format398 may have additional “payload” bits that may be used to provide SIMDvectorization functionality for conditional loops with additional newfeatures such as, for example, a user configurable mask register, or anadditional operand, or selections from among 128-bit, 256-bit or 512-bitvector registers, or more registers from which to select, etc.

For example, where VEX format 397 may be used to provide SIMDvectorization functionality for conditional loops with an implicitconditions mask, the EVEX format 398 may be used to provide SIMDvectorization functionality for conditional loops with an explicit userconfigurable conditions mask. Additionally, where VEX format 397 may beused to provide SIMD vectorization functionality for conditional loopson 128-bit or 256-bit vector registers, EVEX format 398 may be used toprovide SIMD vectorization functionality for conditional loops on128-bit, 256-bit, 512-bit or larger (or smaller) vector registers.

Example vector expand instructions to provide SIMD vectorizationfunctionality for conditional loops are illustrated by the followingexamples:

Instruction destination source1 source2 description VEXPANDD Vmm1/ Vmm2Mask1 Copy data from consecutive 32-bit vector Mem1 elements in thesource vector, Vmm2, into unmasked 32-bit vector elements of thespecified destination vector, Vmm1 or Mem1, according to the maskregister, Mask1, without copying data into masked vector elements of thespecified destination vector. In some embodiments masked destinationvector elements may be zeroed. VEXPANDQ Vmm1/ Vmm2 Mask1 Copy data fromconsecutive 64-bit vector Mem1 elements in the source vector, Vmm2, intounmasked 64-bit vector elements of the specified destination vector,Vmm1 or Mem1, according to the mask register, Mask1, without copyingdata into masked vector elements of the specified destination vector. Insome embodiments masked destination vector elements may be zeroed.VEXPANDD Vmm1 Vmm2/ Mask1 Copy data from consecutive 32-bit vector Mem1elements in the source vector, Vmm2 or Mem1, into unmasked 32-bit vectorelements of the specified destination vector, Vmm1, according to themask register, Mask1, without copying data into masked vector elementsof the specified destination vector. In some embodiments maskeddestination vector elements may be zeroed. VEXPANDQ Vmm1 Vmm2/ Mask1Copy data from consecutive 64-bit vector Mem1 elements in the sourcevector, Vmm2 or Mem1, into unmasked 64-bit vector elements of thespecified destination vector, Vmm1, according to the mask register,Mask1, without copying data into masked vector elements of the specifieddestination vector. In some embodiments masked destination vectorelements may be zeroed.

It will be appreciated that vectorization of the conditional loop may beaccomplished through the use of SIMD vector expand instructions, e.g. asshown in the instructions above, thereby increasing performance andinstruction throughput, and decreasing power use and energy consumption.Techniques using instructions such as these may be employed inapplications, such as online clustering where large amounts ofcontinuously produced data need to be organized under real-timeconditions. Such applications may include network intrusion detection,pattern recognition, and data mining, as well as other usefulapplications, parts of which are not easily vectorized otherwise.

FIG. 4A is a block diagram illustrating an in-order pipeline and aregister renaming stage, out-of-order issue/execution pipeline accordingto at least one embodiment of the invention. FIG. 4B is a block diagramillustrating an in-order architecture core and a register renaminglogic, out-of-order issue/execution logic to be included in a processoraccording to at least one embodiment of the invention. The solid linedboxes in FIG. 4A illustrate the in-order pipeline, while the dashedlined boxes illustrates the register renaming, out-of-orderissue/execution pipeline. Similarly, the solid lined boxes in FIG. 4Billustrate the in-order architecture logic, while the dashed lined boxesillustrates the register renaming logic and out-of-order issue/executionlogic.

In FIG. 4A, a processor pipeline 400 includes a fetch stage 402, alength decode stage 404, a decode stage 406, an allocation stage 408, arenaming stage 410, a scheduling (also known as a dispatch or issue)stage 412, a register read/memory read stage 414, an execute stage 416,a write back/memory write stage 418, an exception handling stage 422,and a commit stage 424.

In FIG. 4B, arrows denote a coupling between two or more units and thedirection of the arrow indicates a direction of data flow between thoseunits. FIG. 4B shows processor core 490 including a front end unit 430coupled to an execution engine unit 450, and both are coupled to amemory unit 470.

The core 490 may be a reduced instruction set computing (RISC) core, acomplex instruction set computing (CISC) core, a very long instructionword (VLIW) core, or a hybrid or alternative core type. As yet anotheroption, the core 490 may be a special-purpose core, such as, forexample, a network or communication core, compression engine, graphicscore, or the like.

The front end unit 430 includes a branch prediction unit 432 coupled toan instruction cache unit 434, which is coupled to an instructiontranslation lookaside buffer (TLB) 436, which is coupled to aninstruction fetch unit 438, which is coupled to a decode unit 440. Thedecode unit or decoder may decode instructions, and generate as anoutput one or more micro-operations, micro-code entry points,microinstructions, other instructions, or other control signals, whichare decoded from, or which otherwise reflect, or are derived from, theoriginal instructions. The decoder may be implemented using variousdifferent mechanisms. Examples of suitable mechanisms include, but arenot limited to, look-up tables, hardware implementations, programmablelogic arrays (PLAs), microcode read only memories (ROMs), etc. Theinstruction cache unit 434 is further coupled to a level 2 (L2) cacheunit 476 in the memory unit 470. The decode unit 440 is coupled to arename/allocator unit 452 in the execution engine unit 450.

The execution engine unit 450 includes the rename/allocator unit 452coupled to a retirement unit 454 and a set of one or more schedulerunit(s) 456. The scheduler unit(s) 456 represents any number ofdifferent schedulers, including reservations stations, centralinstruction window, etc. The scheduler unit(s) 456 is coupled to thephysical register file(s) unit(s) 458. Each of the physical registerfile(s) units 458 represents one or more physical register files,different ones of which store one or more different data types, such asscalar integer, scalar floating point, packed integer, packed floatingpoint, vector integer, vector floating point, etc., status (e.g., aninstruction pointer that is the address of the next instruction to beexecuted), etc. The physical register file(s) unit(s) 458 is overlappedby the retirement unit 454 to illustrate various ways in which registerrenaming and out-of-order execution may be implemented (e.g., using areorder buffer(s) and a retirement register file(s), using a futurefile(s), a history buffer(s), and a retirement register file(s); using aregister maps and a pool of registers; etc.). Generally, thearchitectural registers are visible from the outside of the processor orfrom a programmer's perspective. The registers are not limited to anyknown particular type of circuit. Various different types of registersare suitable as long as they are capable of storing and providing dataas described herein. Examples of suitable registers include, but are notlimited to, dedicated physical registers, dynamically allocated physicalregisters using register renaming, combinations of dedicated anddynamically allocated physical registers, etc. The retirement unit 454and the physical register file(s) unit(s) 458 are coupled to theexecution cluster(s) 460. The execution cluster(s) 460 includes a set ofone or more execution units 462 and a set of one or more memory accessunits 464. The execution units 462 may perform various operations (e.g.,shifts, addition, subtraction, multiplication) and on various types ofdata (e.g., scalar floating point, packed integer, packed floatingpoint, vector integer, vector floating point). While some embodimentsmay include a number of execution units dedicated to specific functionsor sets of functions, other embodiments may include only one executionunit or multiple execution units that all perform all functions. Thescheduler unit(s) 456, physical register file(s) unit(s) 458, andexecution cluster(s) 460 are shown as being possibly plural becausecertain embodiments create separate pipelines for certain types ofdata/operations (e.g., a scalar integer pipeline, a scalar floatingpoint/packed integer/packed floating point/vector integer/vectorfloating point pipeline, and/or a memory access pipeline that each havetheir own scheduler unit, physical register file(s) unit, and/orexecution cluster, and in the case of a separate memory access pipeline,certain embodiments are implemented in which only the execution clusterof this pipeline has the memory access unit(s) 464). It should also beunderstood that where separate pipelines are used, one or more of thesepipelines may be out-of-order issue/execution and the rest in-order.

The set of memory access units 464 is coupled to the memory unit 470,which includes a data TLB unit 472 coupled to a data cache unit 474coupled to a level 2 (L2) cache unit 476. In one exemplary embodiment,the memory access units 464 may include a load unit, a store addressunit, and a store data unit, each of which is coupled to the data TLBunit 472 in the memory unit 470. The L2 cache unit 476 is coupled to oneor more other levels of cache and eventually to a main memory.

By way of example, the exemplary register renaming, out-of-order is corearchitecture may implement the pipeline 400 as follows: 1) theinstruction fetch 438 performs the fetch and length decoding stages 402and 404; 2) the decode unit 440 performs the decode stage 406; 3) therename/allocator unit 452 performs the allocation stage 408 and renamingstage 410; 4) the scheduler unit(s) 456 performs the schedule stage 412;5) the physical register file(s) unit(s) 458 and the memory unit 470perform the register read/memory read stage 414; the execution cluster460 perform the execute stage 416; 6) the memory unit 470 and thephysical register file(s) unit(s) 458 perform the write back/memorywrite stage 418; 7) various units may be involved in the exceptionhandling stage 422; and 8) the retirement unit 454 and the physicalregister file(s) unit(s) 458 perform the commit stage 424.

The core 490 may support one or more instructions sets (e.g., the x86instruction set (with some extensions that have been added with newerversions); the MIPS instruction set of MIPS Technologies of Sunnyvale,Calif.; the ARM instruction set (with optional additional extensionssuch as NEON) of ARM Holdings of Sunnyvale, Calif.).

It should be understood that the core may support multithreading(executing two or more parallel sets of operations or threads), and maydo so in a variety of ways including time sliced multithreading,simultaneous multithreading (where a single physical core provides alogical core for each of the threads that physical core issimultaneously multithreading), or a combination thereof (e.g., timesliced fetching and decoding and simultaneous multithreading thereaftersuch as in the Intel® Hyperthreading technology).

While register renaming is described in the context of out-of-orderexecution, it should be understood that register renaming may be used inan in-order architecture. While the illustrated embodiment of theprocessor also includes a separate instruction and data cache units434/474 and a shared L2 cache unit 476, alternative embodiments may havea single internal cache for both instructions and data, such as, forexample, a Level 1 (L1) internal cache, or multiple levels of internalcache. In some embodiments, the system may include a combination of aninternal cache and an external cache that is external to the core and/orthe processor. Alternatively, all of the cache may be external to thecore and/or the processor.

FIG. 5 is a block diagram of a single core processor and a multicoreprocessor 500 with integrated memory controller and graphics accordingto embodiments of the invention. The solid lined boxes in FIG. 5illustrate a processor 500 with a single core 502A, a system agent 510,a set of one or more bus controller units 516, while the optionaladdition of the dashed lined boxes illustrates an alternative processor500 with multiple cores 502A-N, a set of one or more integrated memorycontroller unit(s) 514 in the system agent unit 510, and an integratedgraphics logic 508.

The memory hierarchy includes one or more levels of cache within thecores, a set or one or more shared cache units 506, and external memory(not shown) coupled to the set of integrated memory controller units514. The set of shared cache units 506 may include one or more mid-levelcaches, such as level 2 (L2), level 3 (L3), level 4 (L4), or otherlevels of cache, a last level cache (LLC), and/or combinations thereof.While in one embodiment a ring based interconnect unit 512 interconnectsthe integrated graphics logic 508, the set of shared cache units 506,and the system agent unit 510, alternative embodiments may use anynumber of well-known techniques for interconnecting such units.

In some embodiments, one or more of the cores 502A-N are capable ofmulti-threading. The system agent 510 includes those componentscoordinating and operating cores 502A-N. The system agent unit 510 mayinclude for example a power control unit (PCU) and a display unit. ThePCU may be or include logic and components needed for regulating thepower state of the cores 502A-N and the integrated graphics logic 508.The display unit is for driving one or more externally connecteddisplays.

The cores 502A-N may be homogenous or heterogeneous in terms ofarchitecture and/or instruction set. For example, some of the cores502A-N may be in order while others are out-of-order. As anotherexample, two or more of the cores 502A-N may be capable of execution thesame instruction set, while others may be capable of executing only asubset of that instruction set or a different instruction set.

The processor may be a general-purpose processor, such as a Core™ i3,i5, i7, 2 Duo and Quad, Xeon™, Itanium™, XScale™ or StrongARM™processor, which are available from Intel Corporation, of Santa Clara,Calif. Alternatively, the processor may be from another company, such asARM Holdings, Ltd, MIPS, etc. The processor may be a special-purposeprocessor, such as, for example, a network or communication processor,compression engine, graphics processor, co-processor, embeddedprocessor, or the like. The processor may be implemented on one or morechips. The processor 500 may be a part of and/or may be implemented onone or more substrates using any of a number of process technologies,such as, for example, BiCMOS, CMOS, or NMOS.

FIGS. 6-8 are exemplary systems suitable for including the processor500, while FIG. 9 is an exemplary system on a chip (SoC) that mayinclude one or more of the cores 502. Other system designs andconfigurations known in the arts for laptops, desktops, handheld PCs,personal digital assistants, engineering workstations, servers, networkdevices, network hubs, switches, embedded processors, digital signalprocessors (DSPs), graphics devices, video game devices, set-top boxes,micro controllers, cell phones, portable media players, hand helddevices, and various other electronic devices, are also suitable. Ingeneral, a huge variety of systems or electronic devices capable ofincorporating a processor and/or other execution logic as disclosedherein are generally suitable.

Referring now to FIG. 6, shown is a block diagram of a system 600 inaccordance with one embodiment of the present invention. The system 600may include one or more processors 610, 615, which are coupled tographics memory controller hub (GMCH) 620. The optional nature ofadditional processors 615 is denoted in FIG. 6 with broken lines.

Each processor 610,615 may be some version of the processor 500.However, it should be noted that it is unlikely that integrated graphicslogic and integrated memory control units would exist in the processors610,615. FIG. 6 illustrates that the GMCH 620 may be coupled to a memory640 that may be, for example, a dynamic random access memory (DRAM). TheDRAM may, for at least one embodiment, be associated with a non-volatilecache.

The GMCH 620 may be a chipset, or a portion of a chipset. The GMCH 620may communicate with the processor(s) 610, 615 and control interactionbetween the processor(s) 610, 615 and memory 640. The GMCH 620 may alsoact as an accelerated bus interface between the processor(s) 610, 615and other elements of the system 600. For at least one embodiment, theGMCH 620 communicates with the processor(s) 610, 615 via a multi-dropbus, such as a frontside bus (FSB) 695.

Furthermore, GMCH 620 is coupled to a display 645 (such as a flat paneldisplay). GMCH 620 may include an integrated graphics accelerator. GMCH620 is further coupled to an input/output (I/O) controller hub (ICH)650, which may be used to couple various peripheral devices to system600. Shown for example in the embodiment of FIG. 6 is an externalgraphics device 660, which may be a discrete graphics device coupled toICH 650, along with another peripheral device 670.

Alternatively, additional or different processors may also be present inthe system 600. For example, additional processor(s) 615 may includeadditional processors(s) that are the same as processor 610, additionalprocessor(s) that are heterogeneous or asymmetric to processor 610,accelerators (such as, e.g., graphics accelerators or digital signalprocessing (DSP) units), field programmable gate arrays, or any otherprocessor. There can be a variety of differences between the physicalresources 610, 615 in terms of a spectrum of metrics of merit includingarchitectural, micro-architectural, thermal, power consumptioncharacteristics, and the like. These differences may effectivelymanifest themselves as asymmetry and heterogeneity amongst theprocessors 610, 615. For at least one embodiment, the various processors610, 615 may reside in the same die package.

Referring now to FIG. 7, shown is a block diagram of a second system 700in accordance with an embodiment of the present invention. As shown inFIG. 7, multiprocessor system 700 is a point-to-point interconnectsystem, and includes a first processor 770 and a second processor 780coupled via a point-to-point interconnect 750. Each of processors 770and 780 may be some version of the processor 500 as one or more of theprocessors 610,615.

While shown with only two processors 770, 780, it is to be understoodthat the scope of the present invention is not so limited. In otherembodiments, one or more additional processors may be present in a givenprocessor.

Processors 770 and 780 are shown including integrated memory controllerunits 772 and 782, respectively. Processor 770 also includes as part ofits bus controller units point-to-point (P-P) interfaces 776 and 778;similarly, second processor 780 includes P-P interfaces 786 and 788.Processors 770, 780 may exchange information via a point-to-point (P-P)interface 750 using P-P interface circuits 778, 788. As shown in FIG. 7,IMCs 772 and 782 couple the processors to respective memories, namely amemory 732 and a memory 734, which may be portions of main memorylocally attached to the respective processors.

Processors 770, 780 may each exchange information with a chipset 790 viaindividual P-P interfaces 752, 754 using point to point interfacecircuits 776, 794, 786, 798. Chipset 790 may also exchange informationwith a high-performance graphics circuit 738 via a high-performancegraphics interface 739.

A shared cache (not shown) may be included in either processor oroutside of both processors, yet connected with the processors via P-Pinterconnect, such that either or both processors' local cacheinformation may be stored in the shared cache if a processor is placedinto a low power mode.

Chipset 790 may be coupled to a first bus 716 via an interface 796. Inone embodiment, first bus 716 may be a Peripheral Component Interconnect(PCI) bus, or a bus such as a PCI Express bus or another thirdgeneration I/O interconnect bus, although the scope of the presentinvention is not so limited.

As shown in FIG. 7, various I/O devices 714 may be coupled to first bus716, along with a bus bridge 718 which couples first bus 716 to a secondbus 720. In one embodiment, second bus 720 may be a low pin count (LPC)bus. Various devices may be coupled to second bus 720 including, forexample, a keyboard and/or mouse 722, communication devices 727 and astorage unit 728 such as a disk drive or other mass storage device whichmay include instructions/code and data 730, in one embodiment. Further,an audio I/O 724 may be coupled to second bus 720. Note that otherarchitectures are possible. For example, instead of the point-to-pointarchitecture of FIG. 7, a system may implement a multi-drop bus or othersuch architecture.

Referring now to FIG. 8, shown is a block diagram of a third system 800in accordance with an embodiment of the present invention Like elementsin FIG. 7 and FIG. 8 bear like reference numerals, and certain aspectsof FIG. 7 have been omitted from FIG. 8 in order to avoid obscuringother aspects of FIG. 8.

FIG. 8 illustrates that the processors 870, 880 may include integratedmemory and I/O control logic (“CL”) 872 and 882, respectively. For atleast one embodiment, the CL 872, 882 may include integrated memorycontroller units such as that described above in connection with FIGS. 5and 7. In addition. CL 872, 882 may also include I/O control logic. FIG.8 illustrates that not only are the memories 832, 834 coupled to the CL872, 882, but also that I/O devices 814 are also coupled to the controllogic 872, 882. Legacy I/O devices 815 are coupled to the chipset 890.

Referring now to FIG. 9, shown is a block diagram of a SoC 900 inaccordance with an embodiment of the present invention. Similar elementsin FIG. 5 bear like reference numerals. Also, dashed lined boxes areoptional features on more advanced SoCs. In FIG. 9, an interconnectunit(s) 902 is coupled to: an application processor 910 which includes aset of one or more cores 502A-N and shared cache unit(s) 506; a systemagent unit 510; a bus controller unit(s) 516; an integrated memorycontroller unit(s) 514; a set of one or more media processors 920 whichmay include integrated graphics logic 508, an image processor 924 forproviding still and/or video camera functionality, an audio processor926 for providing hardware audio acceleration, and a video processor 928for providing video encode/decode acceleration; an static random accessmemory (SRAM) unit 930; a direct memory access (DMA) unit 932; and adisplay unit 940 for coupling to one or more external displays.

FIG. 10 illustrates a processor containing a central processing unit(CPU) and a graphics processing unit (GPU), which may perform at leastone instruction according to one embodiment. In one embodiment, aninstruction to perform operations according to at least one embodimentcould be performed by the CPU. In another embodiment, the instructioncould be performed by the GPU. In still another embodiment, theinstruction may be performed through a combination of operationsperformed by the GPU and the CPU. For example, in one embodiment, aninstruction in accordance with one embodiment may be received anddecoded for execution on the GPU. However, one or more operations withinthe decoded instruction may be performed by a CPU and the resultreturned to the GPU for final retirement of the instruction. Conversely,in some embodiments, the CPU may act as the primary processor and theGPU as the co-processor.

In some embodiments, instructions that benefit from highly parallel,throughput processors may be performed by the GPU, while instructionsthat benefit from the performance of processors that benefit from deeplypipelined architectures may be performed by the CPU. For example,graphics, scientific applications, financial applications and otherparallel workloads may benefit from the performance of the GPU and beexecuted accordingly, whereas more sequential applications, such asoperating system kernel or application code may be better suited for theCPU.

In FIG. 10, processor 1000 includes a CPU 1005, GPU 1010, imageprocessor 1015, video processor 1020, USB controller 1025, UARTcontroller 1030, SPI/SDIO controller 1035, display device 1040,High-Definition Multimedia Interface (HDMI) controller 1045, MIPIcontroller 1050, flash memory controller 1055, dual data rate (DDR)controller 1060, security engine 1065, and I²S/I²C (Integrated InterchipSound/Inter-Integrated Circuit) interface 1070. Other logic and circuitsmay be included in the processor of FIG. 10, including more CPUs or GPUsand other peripheral interface controllers.

One or more aspects of at least one embodiment may be implemented byrepresentative data stored on a machine-readable medium which representsvarious logic within the processor, which when read by a machine causesthe machine to fabricate logic to perform the techniques describedherein. Such representations, known as “IP cores” may be stored on atangible, machine readable medium (“tape”) and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor. For example, IPcores, such as the Cortex™ family of processors developed by ARMHoldings, Ltd. and Loongson IP cores developed the Institute ofComputing Technology (ICT) of the Chinese Academy of Sciences may belicensed or sold to various customers or licensees, such as TexasInstruments, Qualcomm, Apple, or Samsung and implemented in processorsproduced by these customers or licensees.

FIG. 11 shows a block diagram illustrating the development of IP coresaccording to one embodiment. Storage 1130 includes simulation software1120 and/or hardware or software model 1110. In one embodiment, the datarepresenting the IP core design can be provided to the storage 1130 viamemory 1140 (e.g., hard disk), wired connection (e.g., internet) 1150 orwireless connection 1160. The IP core information generated by thesimulation tool and model can then be transmitted to a fabricationfacility where it can be fabricated by a third party to perform at leastone instruction in accordance with at least one embodiment.

In some embodiments, one or more instructions may correspond to a firsttype or architecture (e.g., x86) and be translated or emulated on aprocessor of a different type or architecture (e.g., ARM). Aninstruction, according to one embodiment, may therefore be performed onany processor or processor type, including ARM, x86, MIPS, a GPU, orother processor type or architecture.

FIG. 12 illustrates how an instruction of a first type is emulated by aprocessor of a different type, according to one embodiment. In FIG. 12,program 1205 contains some instructions that may perform the same orsubstantially the same function as an instruction according to oneembodiment. However the instructions of program 1205 may be of a typeand/or format that is different or incompatible with processor 1215,meaning the instructions of the type in program 1205 may not be able tobe executed natively by the processor 1215. However, with the help ofemulation logic, 1210, the instructions of program 1205 are translatedinto instructions that are natively capable of being executed by theprocessor 1215. In one embodiment, the emulation logic is embodied inhardware. In another embodiment, the emulation logic is embodied in atangible, machine-readable medium containing software to translateinstructions of the type in the program 1205 into the type nativelyexecutable by the processor 1215. In other embodiments, emulation logicis a combination of fixed-function or programmable hardware and aprogram stored on a tangible, machine-readable medium. In oneembodiment, the processor contains the emulation logic, whereas in otherembodiments, the emulation logic exists outside of the processor and isprovided by a third party. In one embodiment, the processor is capableof loading the emulation logic embodied in a tangible, machine-readablemedium containing software by executing microcode or firmware containedin or associated with the processor.

FIG. 13 is a block diagram contrasting the use of a software instructionconverter to convert binary instructions in a source instruction set tobinary instructions in a target instruction set according to embodimentsof the invention. In the illustrated embodiment, the instructionconverter is a software instruction converter, although alternativelythe instruction converter may be implemented in software, firmware,hardware, or various combinations thereof. FIG. 13 shows a program in ahigh level language 1302 may be compiled using an x86 compiler 1304 togenerate x86 binary code 1306 that may be natively executed by aprocessor with at least one x86 instruction set core 1316. The processorwith at least one x86 instruction set core 1316 represents any processorthat can perform substantially the same functions as a Intel processorwith at least one x86 instruction set core by compatibly executing orotherwise processing (1) a substantial portion of the instruction set ofthe Intel x86 instruction set core or (2) object code versions ofapplications or other software targeted to run on an Intel processorwith at least one x86 instruction set core, in order to achievesubstantially the same result as an Intel processor with at least onex86 instruction set core. The x86 compiler 1304 represents a compilerthat is operable to generate x86 binary code 1306 (e.g., object code)that can, with or without additional linkage processing, be executed onthe processor with at least one x86 instruction set core 1316.Similarly, FIG. 13 shows the program in the high level language 1302 maybe compiled using an alternative instruction set compiler 1308 togenerate alternative instruction set binary code 1310 that may benatively executed by a processor without at least one x86 instructionset core 1314 (e.g., a processor with cores that execute the MIPSinstruction set of MIPS Technologies of Sunnyvale, Calif. and/or thatexecute the ARM instruction set of ARM Holdings of Sunnyvale, Calif.).The instruction converter 1312 is used to convert the x86 binary code1306 into code that may be natively executed by the processor without anx86 instruction set core 1314. This converted code is not likely to bethe same as the alternative instruction set binary code 1310 because aninstruction converter capable of this is difficult to make; however, theconverted code will accomplish the general operation and be made up ofinstructions from the alternative instruction set. Thus, the instructionconverter 1312 represents software, firmware, hardware, or a combinationthereof that, through emulation, simulation or any other process, allowsa processor or other electronic device that does not have an x86instruction set processor or core to execute the x86 binary code 1306.

FIG. 14A illustrates a flow diagram for one embodiment of a process 1401using an instruction to provide SIMD vectorization functionality forconditional loops. Process 1401 and other processes herein disclosed areperformed by processing blocks that may comprise dedicated hardware orsoftware or firmware operation codes executable by general purposemachines or by special purpose machines or by a combination of both.

In processing block 1410 of process 1401 elements of a counts vector areset to n count values (e.g. 0, 1, 2, . . . , n−1). In processing block1415 values are loaded from an array, Is_Center[i: i+n−1], to obtain adecisions vector, CntrTbl (e.g. t_(i), t_(i+1), t_(i+2), . . . ,t_(i+n−1)). In processing block 1420 the values of the decisions vectorare compared to zeroes using a vector packed compare not-equal(VPCMPNEQ) instruction to generate a vector mask according to thedecisions vector and the vector mask is stored in a mask register. Inprocessing block 1425 responsive to executing a SIMD vector expandinstruction (VEXPAND) having a source parameter to specify the countsvector as a source vector, a mask parameter to specify the maskregister, and a destination parameter to specify a destination vector,CntrTbl, to hold n consecutive vector elements responsive to the SIMDvector expand instruction executed, each of the n consecutive vectorelements having a same variable partition size of m bytes, data fromconsecutive vector elements in the counts vector are copied intounmasked vector elements of the CntrTbl destination vector, withoutcopying data from the counts vector into masked vector elements of theCntrTbl destination vector. Then in processing block 1430 elements ofthe CntrTbl destination vector are stored to the memory array,Center_Table[i: i+n−1], as a result of this iteration of the vectorizedconditional loop. In processing block 1435, n is added to the loopindex, i, and in processing block 1440 it is determined whether or notprocessing is finished. If so, processing ends in processing block 1490.Otherwise processing proceeds to processing block 1445 where the numberof counts, NumCounts, stored to unmasked elements is computed from thevector mask by a population count instruction (POPCNT). Then inprocessing block 1450 that number of counts is broadcast to a vector,VNumCounts, and added to each of the elements of the counts vector inprocessing block 1455 using a vector packed addition instruction(VPADD). Processing then reiterates beginning at processing block 1415.

FIG. 14B illustrates a flow diagram for another embodiment of a process1402 using an instruction to provide SIMD vectorization functionalityfor conditional loops. In processing block 1410 of process 1402 elementsof a counts vector are set to n count values (e.g. 0, 1, 2, . . . ,n−1). In processing block 1415 values are loaded from an array,Is_Center[i: i+n−1], to obtain a decisions vector, CntrTbl (e.g. t_(i),t_(i+1), t_(i+2), . . . , t_(i+n−1)). In processing block 1420 thevalues of the decisions vector are compared to zeroes using a vectorpacked compare not-equal (VPCMPNEQ) instruction to generate a vectormask according to the decisions vector and the vector mask is stored ina mask register. In processing block 1426 responsive to executing a SIMDvector expand instruction (VEXPAND) having a source parameter to specifythe counts vector as a source vector, a mask parameter to specify themask register, and a destination parameter to specify a destinationvector portion of a memory array, Center_Table[i: i+n−1], to hold nconsecutive vector elements responsive to the SIMD vector expandinstruction executed, each of the n consecutive vector elements having asame variable partition size of m bytes; data from consecutive vectorelements in the counts vector are copied into unmasked vector elementsof the Center_Table destination vector, without copying data from thecounts vector into masked vector elements of the Center_Tabledestination vector as a result of this iteration of the vectorizedconditional loop. In processing block 1435, n is added to the loopindex, i, and in processing block 1440 it is determined whether or notprocessing is finished. If so, processing ends in processing block 1490.Otherwise processing proceeds to processing block 1445 where the numberof counts, NumCounts, stored to unmasked elements is computed from thevector mask by a population count instruction (POPCNT). Then inprocessing block 1450 that number of counts is broadcast to a vector,VNumCounts, and added to each of the elements of the counts vector inprocessing block 1455 using a vector packed addition instruction(VPADD). Processing then reiterates beginning at processing block 1415.

It will be appreciated that embodiments of the vector expand instructionthat specify a destination vector in memory may eliminate need for aseparate masked vector write operation, and that vectorization of theconditional loop may be accomplished through the use of instructions andlogic as shown, thereby increasing performance and instructionthroughput, while decreasing energy consumption. These techniques may beemployed in applications, such as online clustering where large amountsof continuously produced data must be organized under real-timeconditions, including network intrusion detection, pattern recognition,and data mining, etc.

FIG. 15A illustrates a flow diagram for one embodiment of a process 1501of executing a vector expand instruction to provide SIMD vectorizationfunctionality for conditional loops. In processing block 1510 of process1502 a SIMD vector expand instruction is received. Embodiments of theSIMD vector expand instruction may include a source argument to identifya source vector, a destination argument and a mask argument to specify adestination vector and a mask register to hold a plurality of nconsecutive vector elements and n associated conditional mask elementsrespectively, wherein n varies responsive to the SIMD vector expandinstruction. In processing block 1520 the next mask field is read fromthe mask register and the next conditional mask element is checked inprocessing block 1530 to determine if it is of a first value (e.g. oneor non-zero). If so processing proceeds to processing block 1540 wheredata from the next consecutive vector element of the source vector iscopied into the unmasked vector element of the destination vectorcorresponding to the current mask field. If, on the other hand, the nextconditional mask element is not of the first value (e.g. it is zero)then processing proceeds instead to processing block 1550 where the nextvector element of the destination vector (which is masked) will beskipped. Then processing proceeds to processing block 1560 where it isdetermined if processing is finished, and if so processing ends inprocessing block 1590. Otherwise processing reiterates beginning atprocessing block 1520.

FIG. 15B illustrates a flow diagram for another embodiment of a process1502 of executing a vector expand instruction to provide SIMDvectorization functionality for conditional loops. In processing block1510 of process 1502 a SIMD vector expand instruction is received.Embodiments of the SIMD vector expand instruction may include a sourceargument to identify a source vector, a destination argument and a maskargument to specify a destination vector and a mask register to hold aplurality of n consecutive vector elements and n associated conditionalmask elements respectively, wherein again n varies responsive to theSIMD vector expand instruction. In processing block 1520 the next maskfield is read from the mask register and the next conditional maskelement is checked in processing block 1530 to determine if it is of afirst value (e.g. one or non-zero). If so processing proceeds toprocessing block 1540 where data from the next consecutive vectorelement of the source vector is copied into the unmasked vector elementof the destination vector corresponding to the current mask field. If,on the other hand, the next conditional mask element is not of the firstvalue (e.g. if it is zero) then processing proceeds instead toprocessing block 1555 where a predetermined value (e.g. zero) is writtento the masked vector element of the destination vector corresponding tothe current mask field. Then processing proceeds to processing block1560 where it is determined if processing is finished, and if soprocessing ends in processing block 1590. Otherwise processingreiterates beginning at processing block 1520.

FIG. 16 illustrates a flow diagram for one embodiment of a process 1601to provide SIMD vectorization functionality for conditional loops. Inprocessing block 1610 of process 1601 elements of a vector of counts areinitialized to n count values (e.g. 0, 1, 2, . . . , n−1). In processingblock 1620 a decisions vector is obtained for a count table, e.g. fromthe array Is_Center[i: i+n−1]. In processing block 1630 the decisionsvector is compared to a vector of expected values, generating a vectormask and storing the vector mask in a mask register. In processing block1640 responsive to a SIMD vector expand instruction (e.g. having asource parameter to specify the vector of counts as a source vector, amask parameter to specify the mask register, and a destination parameterto specify a destination vector portion) data from consecutive vectorelements in the counts vector are expanded and copied into unmaskedvector elements of the count table destination vector portion accordingto the vector mask, without copying data from the counts vector intomasked vector elements of the count table destination vector. Inprocessing block 1650 it is determined whether or not processing isfinished. If so, processing ends in processing block 1690. Otherwiseprocessing proceeds to processing block 1660 where the number ofpositive decisions is counted, and then added to each of the elements ofthe vector of counts in processing block 1670. Processing thenreiterates beginning at processing block 1620.

It will be appreciated that vectorization of the conditional loop may beaccomplished through the use of instructions (e.g. SIMD vector expandinstructions) and logic as disclosed herein, thereby increasingperformance and instruction throughput, and decreasing power use andenergy consumption. These techniques may be employed in applicationssuch as online clustering where large amounts of continuously produceddata must be organized under real-time conditions. Such applicationsinclude network intrusion detection, pattern recognition, and datamining, etc.

FIG. 17 illustrates an embodiment of an apparatus 1701 for executing avector expand instruction to provide SIMD vectorization functionalityfor conditional loops. Embodiments of apparatus 1701 may also be part ofa pipeline 400 (e.g. execution stage 416) or part of a core 490 (e.g.execution unit(s) 462) for execution of an instruction to provide SIMDvector expand functionality. Embodiments of apparatus 1701 may becoupled with a decode stage (e.g. decode 406) or a decoder (e.g. decodeunit 440) to decode an instruction for a SIMD vector expansion, whichmay permit efficient vectorization of conditional loops Embodiments ofone or more execution units (e.g. execution apparatus 1701) responsiveto the decoded instruction, copy data from a portion of n consecutivevector elements in the source vector 1710 into unmasked vector elementsof the destination vector 1760 without copying data from the sourcevector 1710 into masked vector elements of n vector elements in thedestination vector 1760, wherein n varies responsive to the SIMD vectorexpand instruction being executed.

For example, embodiments of apparatus 1701 may be coupled with vectorregisters (e.g. physical register files unit(s) 458) comprising avariable plurality of n variable sized data fields to store values of avariable plurality of n variable sized data elements. Embodiments of theinstruction to provide SIMD vector expansion functionality specify avector expand operation and a data field size for performing the SIMDvector expand for each data field of the memory vector operand and/orvector register (e.g. 1760 and/or 1710) and copy data from a portion ofn consecutive vector elements in the source vector 1710 into unmaskedvector elements of the destination vector 1760 without copying data fromthe source vector 1710 into masked vector elements of the destinationvector 1760.

For example, one embodiment of apparatus 1701 for executing aninstruction to provide SIMD vector expansion functionality reads thevalues of each of the data fields of a vector mask 1720 and copies datafrom a least significant portion of consecutive vector elements of afirst size (e.g. 32-bits or 64-bits) in the source vector 1710,expanding them using expansion multiplexer logic, e.g. multiplexer logic1730-1750 of unmasked expansion circuit 1703, and stores them inunmasked vector elements of the destination vector 1760 in a portion ofa memory vector operand or a vector register. In one embodiment maskedvector elements of the destination vector 1760 are selected as not beingoverwritten, e.g. by control logic 1779 through multiplexer logic1770-1775 of masked expansion circuit 1770, or alternatively as beingoverwritten by a zero value 1778. It will be appreciated that someembodiments of a SIMD vector expand instruction may specify adestination vector in memory thereby eliminating the need for a separatemasked vector write (e.g. maskstore) operation.

FIG. 18 illustrates another embodiment of an apparatus 1801 forexecuting a vector expand instruction to provide SIMD vectorizationfunctionality for conditional loops. Apparatus 1801 comprises anexecution engine unit 1850 and a memory unit 1870. The execution engineunit 1850 includes the rename/allocator unit 1852 coupled to a set ofone or more scheduler unit(s) 1856. The scheduler unit(s) 1856represents any number of different schedulers, including reservationsstations, central instruction window, etc. The scheduler unit(s) 1856 iscoupled to the physical register file(s) including vector physicalregisters 1884, mask physical registers 1882 and integer physicalregisters 1886. Each of the physical register file(s) represents one ormore physical register files, different ones of which store one or moredifferent data types, such as scalar integer, scalar floating point,packed integer, packed floating point, vector integer, vector floatingpoint, etc., status (e.g., an instruction pointer that is the address ofthe next instruction to be executed), etc.

Execution engine unit 1850 of apparatus 1801 comprises an index array1888 to store a set of indices 1830 from a SIMD vector expandinstruction and a corresponding set of mask 1820 elements from the maskphysical registers 1882. For one embodiment a wide vector store channel(e.g. 128-bit, or 256-bit, or 512-bit or larger) and a 64-bitinteger-stack channel may be repurposed to facilitate a transfer ofindices 1830 and mask 1820 elements to index array 1888 (e.g. using oneor more micro-operations). Some embodiments of execution engine unit1850 also comprise a store data buffer 1899 wherein all of the dataelements from a SIMD vector register 1810 for a vector expand operationmay be expanded (e.g. as shown in apparatus 1701) to an intermediatedestination data 1860 and written into multiple individual elementstorage locations of the store data buffer 1899 at one time (e.g. usinga single micro-operation). It will be appreciated that data elementsstored in these multiple individual storage locations of the store databuffer 1899 may then be forwarded to satisfy newer load operationswithout accessing external memory. Finite state machine 1892 isoperatively coupled with the index array 1888 to facilitate a vectorexpand operation using the set of indices 1830 and the correspondingmask 1820 elements.

Address generation logic 1894 in response to finite state machine 1892,generates an effective address 1806 from at least a base address 1840provided by integer physical registers 1886 and an index 1850 of the setof indices 1830 in the index array 1888 for at least each correspondingmask 1820 element having a first value. Storage is allocated in storedata buffer 1899 to hold the data 1860 elements corresponding to thegenerated effective addresses 1806 for storing to corresponding memorylocations by the memory access unit(s) 1864. Data 1860 elementscorresponding to the effective addresses 1806 being generated are copiedto the buffer store data buffer 1899. Memory access unit(s) 1864 areoperatively coupled with the address generation logic 1894 to access amemory location, for a corresponding mask 1807 element having a firstvalue, through memory unit 1870, the memory location corresponding to aneffective address 1806 generated by address generation logic 1894 inresponse to finite state machine 1892, to store a data element 1809. Inone embodiment, the data 1860 elements stored in store data buffer 1899may be accessed to satisfy newer load instructions out of sequentialinstruction order if their effective addresses 1806 correspond to theeffective addresses of the newer load instructions. Finite state machine1892 may then change the corresponding mask 1802 element from the firstvalue to a second value upon successfully storing the expanded dataelement 1809 to memory. In some embodiments successful completion of thevector expand operation may be accomplished through the execution of amicro-operation. In some embodiments such a micro-operation may then beretired upon successful completion (e.g. without faulting) of thecorresponding stores of expanded data 1860 elements by the finite statemachine 1892.

It will be appreciated that in some embodiments, determinations may bemade whether the data 1860 elements stored in store data buffer 1899 mayeventually be used to satisfy newer load instructions out of sequentialinstruction order as early as the storage is allocated in store databuffer 1899 corresponding to the generated effective addresses 1806. Itwill also be appreciated that by scheduling just a few micro-operationsto transfer a set of indices 1830 and a corresponding set of mask 1820elements from the mask physical registers 1882 to index array 1888 andinitialize finite state machine 1892 to expand those fewmicro-operations to store the data 1860, in parallel or concurrentlywith the execution of other instructions and responsive to, and/or insupport of vector expand operations, instruction throughput may beimproved, especially for vectorized conditional loops, therebyincreasing performance and decreasing power use and energy consumption.These techniques may be employed in applications, for example such asonline clustering where large amounts of continuously produced data needto be organized under real-time conditions. Such applications mayinclude network intrusion detection, pattern recognition, and datamining, as well as other similar types of real-time online clusteringapplications.

Embodiments of the mechanisms disclosed herein may be implemented inhardware, software, firmware, or a combination of such implementationapproaches. Embodiments of the invention may be implemented as computerprograms or program code executing on programmable systems comprising atleast one processor, a storage system (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device.

Program code may be applied to input instructions to perform thefunctions described herein and generate output information. The outputinformation may be applied to one or more output devices, in knownfashion. For purposes of this application, a processing system includesany system that has a processor, such as, for example; a digital signalprocessor (DSP), a microcontroller, an application specific integratedcircuit (ASIC), or a microprocessor.

The program code may be implemented in a high level procedural or objectoriented programming language to communicate with a processing system.The program code may also be implemented in assembly or machinelanguage, if desired. In fact, the mechanisms described herein are notlimited in scope to any particular programming language. In any case,the language may be a compiled or interpreted language.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

Such machine-readable storage media may include, without limitation,non-transitory, tangible arrangements of articles manufactured or formedby a machine or device, including storage media such as hard disks, anyother type of disk including floppy disks, optical disks, compact diskread-only memories (CD-ROMs), compact disk rewritable's (CD-RWs), andmagneto-optical disks, semiconductor devices such as read-only memories(ROMs), random access memories (RAMs) such as dynamic random accessmemories (DRAMs), static random access memories (SRAMs), erasableprogrammable read-only memories (EPROMs), flash memories, electricallyerasable programmable read-only memories (EEPROMs), magnetic or opticalcards, or any other type of media suitable for storing electronicinstructions.

Accordingly, embodiments of the invention also include non-transitory,tangible machine-readable media containing instructions or containingdesign data, such as Hardware Description Language (HDL), which definesstructures, circuits, apparatuses, processors and/or system featuresdescribed herein. Such embodiments may also be referred to as programproducts.

In some cases, an instruction converter may be used to convert aninstruction from a source instruction set to a target instruction set.For example, the instruction converter may translate (e.g., using staticbinary translation, dynamic binary translation including dynamiccompilation), morph, emulate, or otherwise convert an instruction to oneor more other instructions to be processed by the core. The instructionconverter may be implemented in software, hardware, firmware, or acombination thereof. The instruction converter may be on processor, offprocessor, or part on and part off processor.

Thus, techniques for performing one or more instructions according to atleast one embodiment are disclosed. While certain exemplary embodimentshave been described and shown in the accompanying drawings, it is to beunderstood that such embodiments are merely illustrative of and notrestrictive on the broad invention, and that this invention not belimited to the specific constructions and arrangements shown anddescribed, since various other modifications may occur to thoseordinarily skilled in the art upon studying this disclosure. In an areaof technology such as this, where growth is fast and furtheradvancements are not easily foreseen, the disclosed embodiments may bereadily modifiable in arrangement and detail as facilitated by enablingtechnological advancements without departing from the principles of thepresent disclosure or the scope of the accompanying claims.

What is claimed is:
 1. A processor comprising: mask physical registersto store a vector conditions mask; vector physical registers to store: asource vector having a first plurality of n data fields having avariable partition size of m bytes, and a destination vector comprisinga second plurality of data fields corresponding to the first pluralityof data fields, wherein each of the second plurality of data fields inthe destination vector corresponds to a mask value in said vectorconditions mask; a decode stage to decode processor instructions, afirst processor instruction specifying a vector expand operation and adata partition size; and one or more execution units, responsive to thedecoded processor instructions, to: set elements of the source vector ton count values; obtain a decisions vector; generate said vectorconditions mask according to the decisions vector; and responsive toexecuting the first processor instruction, copy data from consecutivevector elements in the source vector, into unmasked vector elements ofthe destination vector, without copying data from the source vector intomasked vector elements of the destination vector.
 2. The processor ofclaim 1, wherein the data is copied into unmasked vector elements of thedestination vector and any masked vector elements of the destinationvector are set to a value of zero.
 3. The processor of claim 1, whereinthe data is copied into unmasked vector elements of the destinationvector without modifying any masked vector elements of the destinationvector.
 4. The processor of claim 1, wherein when the first processorinstruction executed expands 32-bit integers, and n is a value selectedfrom the group consisting of 4, 8 and
 16. 5. The processor of claim 1,wherein when the first processor instruction executed expands 64-bitintegers, and n is a value selected from the group consisting of 2, 4and
 8. 6. A computer implemented method comprising: receiving a set ofprocessor instructions, a first processor instruction of the setspecifying: a vector expand operation, a source vector, a mask registerto hold a vector conditions mask, and a destination vector to hold aplurality of n consecutive vector elements, each having a variablepartition size of m bytes; and in response to receiving the set ofprocessor instructions, setting elements of the source vector to n countvalues; obtaining a decisions vector; generating said vector conditionsmask according to the decisions vector; and executing the firstprocessor instruction, by copying data from consecutive vector elementsin the source vector, into unmasked vector elements of the specifieddestination vector, without copying data into masked vector elements ofthe specified destination vector.
 7. The method of claim 6, wherein thedata is copied into unmasked vector elements of the destination vectorand any masked vector elements of the destination vector are set to avalue of zero.
 8. The method of claim 6, wherein the data is copied intounmasked vector elements of the destination vector without modifying anymasked vector elements of the destination vector.
 9. The method of claim6, wherein the source vector is a source vector register.
 10. The methodof claim 6, wherein the destination vector is a destination vectorregister.
 11. A processing system comprising: a memory unit; and aplurality of processors including a first processor core and a secondprocessor core each processor comprising: mask physical registers tostore a vector conditions mask; vector physical registers to store: asource vector having a first plurality of n data fields, each having avariable partition size of m bytes, and a destination vector comprisinga second plurality of data fields corresponding to the first pluralityof data fields, wherein each of the second plurality of data fields inthe destination vector corresponds to a mask value in said vectorconditions mask; a decode stage to decode processor instructions, afirst processor instruction specifying a vector expand operation and adata partition size; and one or more execution units, responsive to thedecoded processor instructions, to: set elements of the source vector ton count values; obtain a decisions vector; generate said vectorconditions mask according to the decisions vector; and responsive toexecuting the first processor instruction, copy data from consecutivevector elements in the source vector, into unmasked vector elements ofthe destination vector, without copying data from the source vector intomasked vector elements of the destination vector.
 12. The processingsystem of claim 11, wherein the data is copied into unmasked vectorelements of the destination vector and any masked vector elements of thedestination vector are set to a value of zero.
 13. The processing systemof claim 11, wherein the data is copied into unmasked vector elements ofthe destination vector without modifying any masked vector elements ofthe destination vector.
 14. The processing system of claim 11, whereinwhen the first processor instruction executed expands 32-bit integers,and n is a value selected from the group consisting of 4, 8 and
 16. 15.The processing system of claim 11, wherein when the first processorinstruction executed expands 64-bit integers, and n is a value selectedfrom the group consisting of 2, 4 and
 8. 16. A non-transitorymachine-readable storage medium storing instructions which, whenexecuted, cause a processing device to perform operations comprising:receiving a set of processor instructions, a first processor instructionof the set specifying: a vector expand operation, a source vector, amask register to hold a vector conditions mask, and a destination vectorto hold a plurality of n consecutive vector elements, each having avariable partition size of m bytes; and in response to receiving the setof processor instructions, setting elements of the source vector to ncount values; obtaining a decisions vector; generating said vectorconditions mask according to the decisions vector; and executing thefirst processor instruction, by copying data from consecutive vectorelements in the source vector, into unmasked vector elements of thespecified destination vector, without copying data into masked vectorelements of the specified destination vector.
 17. The non-transitorymachine-readable medium of claim 16, wherein the data is copied intounmasked vector elements of the destination vector and any masked vectorelements of the destination vector are set to a value of zero.
 18. Thenon-transitory machine-readable medium of claim 16, wherein the data iscopied into unmasked vector elements of the destination vector withoutmodifying any masked vector elements of the destination vector.
 19. Thenon-transitory machine-readable medium of claim 16, wherein the sourcevector is a source vector register.
 20. The non-transitorymachine-readable medium of claim 16, wherein the destination vector is adestination vector register.